Harnessing Digital Health to Objectively Assess Functional Performance in Veterans with Chronic Obstructive Pulmonary Disease.

INTRODUCTION: An early detection of impaired functional performance is critical to enhance symptom management for patients with chronic obstructive pulmonary disease (COPD). However, conventional functional measures based on walking assessments are often impractical for small clinics where the available space to administrate gait-based test is limited. This study examined the feasibility and effectiveness of an upper-extremity frailty meter (FM) in identifying digital measures of functional performance and assessing frailty in COPD patients. METHODS: Forty-eight patients with COPD (age = 68.8 ± 8.5 years, body mass index [BMI] = 28.7 ± 5.8 kg/m2) and 49 controls (age = 70.0 ± 3.0 years, BMI = 28.7 ± 6.1 kg/m2) were recruited. All participants performed a 20-s repetitive elbow flexion-extension test using a wrist-worn FM sensor. Functional performance was quantified by FM metrics, including speed (slowness), range of motion (rigidity), power (weakness), flexion and extension time (slowness), as well as speed and power reduction (exhaustion). Conventional functional measures, including timed-up-and-go test, gait and balance tests, and 5 repetition sit-to-stand test, were also performed. RESULTS: Compared to controls, COPD patients exhibited deteriorated performances in all conventional functional assessments (d = 0.64-1.26, p < 0.010) and all FM metrics (d = 0.45-1.54, p < 0.050). FM metrics had significant agreements with conventional assessment tools (|r| = 0.35-0.55, p ≤ 0.001). FM metrics efficiently identified COPD patients with pre-frailty and frailty (d = 0.82-2.12, p < 0.050). CONCLUSION: This study proposes the feasibility of using a 20-s repetitive elbow flexion-extension test and wrist-worn sensor-derived frailty metrics as an alternative and practical solution to evaluate functional performance in COPD patients. Its simplicity and low risk for test administration may also facilitate its application for remote patient monitoring. Furthermore, in settings where the administration of walking test is impractical, for example, when ventilator support is needed or space is limited, FM may be used as an alternative solution. Future studies are encouraged to use the FM to quantitatively monitor the progressive decline in functional performance and quantify outcomes of rehabilitation interventions.

Remote Physical Frailty Monitoring-The Application of Deep Learning-Based Image Processing in Tele-Health.

Remote screening physical frailty (PF) may assist in triaging patients with chronic obstructive pulmonary disease (COPD) who are in clinical priorities to visit a clinical center for preventive care. Conventional PF assessment tools have however limited feasibility for remote patient monitoring applications. To improve the safety of PF assessment, we previously developed and validated a quick and safe PF screening tool called Frailty Meter (FM). FM works by quantifying weakness, slowness, rigidity, and exhaustion during a 20-second repetitive elbow flexion/extension task using a wrist-worn sensor and generates a frailty index (FI) ranging from zero to one; higher values indicate progressively greater severity of frailty. However, the use of wrist-sensor limits its applications in telemedicine and remote patient monitoring. In this study, we developed a sensor-less FM based on deep learning-based image processing, which can be easily integrated into mobile health and enables remote assessment of physical frailty. The sensor-less FM extracts kinematic features of the forearm motion from the video of 20-second elbow flexion and extension recorded by a tablet camera, and then calculates frailty phenotypes and FI. To test the validity of sensor-less FM, 11 COPD patients admitted to a Telehealth pulmonary rehabilitation clinic and 10 healthy young volunteers (controls) were recruited. All participants completed the test indicating high feasibility. Strong correlations (0.72 < r < 0.99) were observed between the sensor-based FM and sensor-less FM to extract all frailty phenotypes and FI. After adjusting with age and body mass index(BMI), sensor-less FM enables distinguishing COPD group from controls (p<0.050) with the largest effect sizes observed for weakness (Cohen's effect size d=2.24), frailty index (d=1.70), and slowness (d=1.70). These pilot findings suggest feasibility and proof of concept validity of this sensor-less FM toward remote assessment of PF in COPD patients.

Telehealth Pulmonary Rehabilitation for Patients With Severe Chronic Obstructive Pulmonary Disease.

For patients with chronic obstructive pulmonary disease, a home-based, interactive telehealth program can improve accessibility to pulmonary rehabilitation and reduce travel costs.